Behavioral biomarkers for prediction of mortality and seizures in epilepsy - There are 65 million people worldwide with epilepsy and 150,000 new cases of epilepsy are diagnosed in the US annually. Treatment options for epilepsy remain inadequate, with many children and adults with epilepsy suffering from treatment-resistant seizures and are at increased risk for mortality. A major, long- standing, currently unmet challenge for preclinical epilepsy research is to develop and test novel biomarkers that could identify individual animals that are the most likely to develop epilepsy after brain trauma and suffer mortality, as well as monitor, predict and control seizures in chronically epileptic animals in a non-invasive manner. Recently, in a synergistic, collaborative effort, the Soltesz and Datta labs showed that artificial intelligence (AI)-assisted 3D video analysis of spontaneous mouse behaviors can automatically phenotype and sort non-epileptic versus epileptic mice in a purely data-driven manner without observer bias, revealing hidden behavioral phenotypes for both acquired and genetic epilepsies, stages of epileptogenesis, and drug treatments. This methodology, called Motion Sequencing (MoSeq), breaks down complex animal behaviors into stereotyped modules that follow each other with characteristic transition probabilities at sub-second timescales. Here we propose to use mouse models of temporal lobe epilepsy and Dravet Syndrome in combination with various cutting-edge MoSeq-based approaches to identify novel, non-invasive, predictive behavioral biomarkers for chronic seizures and mortality purely from video recordings of spontaneously behaving animals. In addition, we propose to examine the neuronal dynamics underlying the altered expression patterns of behavioral modules in chronic epilepsy in vivo. Finally, we propose to adapt MoSeq for prolonged 24/7 epilepsy monitoring of mice under realistic environmental conditions and social settings, and test non- invasive focused ultrasound technology for innovative closed-loop interventions triggered by the appearance of predictive behavioral biomarkers to control seizures. We anticipate that the results from this research will have a potentially transformative effect on the field by demonstrating the feasibility and power of automated, objective, user-independent, predictive behavioral biomarkers for the epilepsies.
